Scholarlink

<p>Microscopic impurities can contaminate tea during production, processing, and packaging. Current technologies remove only visible contaminants, leaving microscopic foreign objects that compromise tea quality, and reliable detection methods remain lacking. To address this challenge, we propose YOLOv11-PFT, an improved deep learning model based on YOLOv11, enhanced with Powerful-IoU loss, FasterNet, and Triple Attention modules to boost detection accuracy, reduce model size, and improve feature extraction. The resulting lightweight model achieves 99.16% detection accuracy for microscopic tea contaminants, with Precision, Recall, <i>F</i><sub>1</sub> score, and mAP all near 98.7–99.2%, GFLOPs of 5.5, inference speed of 340.6 FPS, and a model size of only 5.0 MB. It outperforms seven benchmark models in accuracy. YOLOv11-PFT offers an effective solution for microscopic contaminant detection in tea, supporting automation in food safety, intelligent quality control, and edge-device deployment in agriculture.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Non-destructive detection of micro-impurities in tea using the YOLOv11-PFT model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

查看全文
  • Zejun Wang,
  • Chun Wang,
  • Wenxia Yuan,
  • Xiujuan Deng,
  • Houqiao Wang,
  • Tianyu Wu,
  • Jinyan Zhao,
  • Weihao Liu,
  • Baijuan Wang

摘要

Microscopic impurities can contaminate tea during production, processing, and packaging. Current technologies remove only visible contaminants, leaving microscopic foreign objects that compromise tea quality, and reliable detection methods remain lacking. To address this challenge, we propose YOLOv11-PFT, an improved deep learning model based on YOLOv11, enhanced with Powerful-IoU loss, FasterNet, and Triple Attention modules to boost detection accuracy, reduce model size, and improve feature extraction. The resulting lightweight model achieves 99.16% detection accuracy for microscopic tea contaminants, with Precision, Recall, F1 score, and mAP all near 98.7–99.2%, GFLOPs of 5.5, inference speed of 340.6 FPS, and a model size of only 5.0 MB. It outperforms seven benchmark models in accuracy. YOLOv11-PFT offers an effective solution for microscopic contaminant detection in tea, supporting automation in food safety, intelligent quality control, and edge-device deployment in agriculture.